本文为美国伍斯特理工学院（作者：Chen Shen）的硕士论文，共70页。
多输入多输出（MIMO）技术在通信系统中得到了广泛的研究。与单输入单输出（SISO）通信相比，它具有更高的吞吐量、更有效的频谱利用率，是现代无线通信中最重要的技术之一。在MIMO系统中，球体检测（sphere detection）是最基本的部分。传统球体检测的目的是实现MIMO系统的最大似然（ML）解调。然而，随着卷积码、Turbo码和LDPC码等先进前向纠错技术的发展，能够为外部译码器提供软信息的球体检测算法受到越来越多的关注。考虑到产生软信息的计算复杂性，开发一种用于MIMO检测的高速VLSI体系结构具有重要意义。
本文的第一部分是关于MIMO球体检测算法。介绍了两种球体检测算法，提出了一种深度优先的Schnorr-Euchner（SE）算法和宽度优先的K-BEST算法，该算法只产生接近ML检测的输出解，但实现效率较高。在这些算法的基础上，提出了一种改进的近似ML算法，该算法与传统的K-Best算法相比，具有较低的复杂度和有限的性能损失。
第二部分是硬件设计。设计并实现了一个能同时产生软信息和硬决策解决方案的4×4 16 QAM MIMO检测系统。采用全流水线并行结构，吞吐量可达3.7Gbps。在这部分中，将改进的近似ML算法作为检测器来生成硬输出和候选列表，然后，设计了一个软信息计算模块，使检测器成功输出，并产生每一位的对数似然比（LLR）值作为软输出。
Multiple-input multiple-output (MIMO) technique in communication system has been widely researched. Compared with single-input single-output (SISO) communication, its properties of higher throughput, more efficient spectrum and usage make it one of the most significant technology in modern wireless communications. In MIMO system, sphere detection is the fundamental part. The purpose of traditional sphere detection is to achieve the maximum likelihood (ML) demodulation of the MIMO system. However, with the development of advanced forward error correction (FEC) techniques, such as the Convolutional code, Turbo code and LDPC code, the sphere detection algorithms that can provide soft information for the outer decoder attract more interests recently. Considering the computing complexity of generating the soft information, it is important to develop a high-speed VLSI architecture for MIMO detection. The first part of this thesis is about MIMO sphere detection algorithms. Two sphere detection algorithms are introduced. The depth first Schnorr-Euchner (SE) algorithm which generates the ML detection solution and the width first K-BEST algorithm, which only generates the nearly-ML detection solution but more efficient in implementation are presented. Based on these algorithms, an improved nearly-ML algorithm with lower complexity and limited performance lose, compared with traditional K-BEST algorithms, is presented. The second part is focused on the hardware design. A 4×4 16 QAM MIMO detection system which can generate both soft information and hard decision solution is designed and implemented in FPGA. With the fully pipelined and parallel structure, it can achieve a throughput of 3.7 Gbps. In this part, the improved nearly-ML algorithm is implmented as a detector to generat both the hard output and candidate list. Then, a soft information calculation block is designed to succeed the detector and produce the log-likelihood ratio (LLR) values for every bit as the soft output.
1 引言
2 球体检测算法和预处理
3 分组排序算法及实现
4 软输出MIMO检测
5 改进的MIMO检测器硬件架构
6 结论与未来工作展望
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